Search results for: hierarchal regression analysis

Authors: Michael K.O. Ayomoh, Khaled A. Abou-El-Hossein., Sameh F.M. Ghobashy

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This paper proposes a numerical modelling scheme for surface roughness prediction. The approach is premised on the use of 3D difference analysis method enhanced with the use of feedback control loop where a set of adaptive weights are generated. The surface roughness values utilized in this paper were adapted from [1]. Their experiments were carried out using S55C high carbon steel. A comparison was further carried out between the proposed technique and those utilized in [1]. The experimental design has three cutting parameters namely: depth of cut, feed rate and cutting speed with twenty-seven experimental sample-space. The simulation trials conducted using Matlab software is of two sub-classes namely: prediction of the surface roughness readings for the non-boundary cutting combinations (NBCC) with the aid of the known surface roughness readings of the boundary cutting combinations (BCC). The following simulation involved the use of the predicted outputs from the NBCC to recover the surface roughness readings for the boundary cutting combinations (BCC). The simulation trial for the NBCC attained a state of total stability in the 7th iteration i.e. a point where the actual and desired roughness readings are equal such that error is minimized to zero by using a set of dynamic weights generated in every following simulation trial. A comparative study among the three methods showed that the proposed difference analysis technique with adaptive weight from feedback control, produced a much accurate output as against the abductive and regression analysis techniques presented in this.

Keywords: Difference Analysis, Surface Roughness; Mesh- Analysis, Feedback control, Adaptive weight, Boundary Element

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Authors: Wenpeng You, Maciej Henneberg

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Background: Sugar consumption has been overwhelmingly advocated as a major dietary offender to obesity prevalence. Meat intake has been hypothesized as an obesity contributor in previous publications, but a moderate amount of meat to be included in our daily diet still has been suggested in many dietary guidelines. Comparable sugar and meat exposure data were obtained to assess the difference in relationships between the two major food groups and obesity prevalence at population level. Methods: Population level estimates of obesity and overweight rates, per capita per day exposure of major food groups (meat, sugar, starch crops, fibers, fats and fruits) and total calories, per capita per year GDP, urbanization and physical inactivity prevalence rate were extracted and matched for statistical analysis. Correlation coefficient (Pearson and partial) comparisons with Fisher’s r-to-z transformation and β range (β ± 2 SE) and overlapping in multiple linear regression (Enter and Stepwise) were used to examine potential differences in the relationships between obesity prevalence and sugar exposure and meat exposure respectively. Results: Pearson and partial correlations (controlled for total calories, physical inactivity prevalence, GDP and urbanization) analyses revealed that sugar and meat exposures correlated to obesity and overweight prevalence significantly. Fisher's r-to-z transformation did not show statistically significant difference in Pearson correlation coefficients (z=-0.53, p=0.5961) or partial correlation coefficients (z=-0.04, p=0.9681) between obesity prevalence and both sugar exposure and meat exposure. Both Enter and Stepwise models in multiple linear regression analysis showed that sugar and meat exposure were most significant predictors of obesity prevalence. Great β range overlapping in the Enter (0.289-0.573) and Stepwise (0.294-0.582) models indicated statistically sugar and meat exposure correlated to obesity without significant difference. Conclusion: Worldwide sugar and meat exposure correlated to obesity prevalence at the same extent. Like sugar, minimal meat exposure should also be suggested in the dietary guidelines.

Keywords: meat, sugar, obesity, energy surplus, meat protein, fats, insulin resistance

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Authors: Tao Zhang

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Job stress appears nearly everywhere especially in the hospitality industry because employees in this industry usually have to work long time and try to meet conflicting demands of their customers, managers, and company. To reduce job stress, employees of casino hotels try to perform leisure activities or tourism. However, casino employees often meet many obstacles or constraints when they plan to travel. Until now, there is little understanding as to why casino hotel employees often face many travel constraints or leisure barriers. What is more, few studies explore the relationship between travel constraints and job stress of casino employees. Therefore, this study is to explore the construct of casino hotel employees' travel constraints and the relationship among job stress, travel constraints, and job satisfaction. Using convenient sampling method, this study planned to investigate 500 front line employees and managers of ten casino hotels in Macau. A total of 500 questionnaires were distributed, and 414 valid questionnaires were received. The return rate of valid questionnaires is 82.8%. Several statistical techniques such as factor analysis, t-test, one-way ANOVA, and regression analysis were applied to analyze the collected data. The findings of this study are as follows. Firstly, by using factor analysis, this study found the travel constraints of casino employees include intrapersonal constraints, interpersonal constraints, and structural constraints. Secondly, by using regression analysis, the study found travel constraints are positively related with job stress while negatively related with job satisfaction. This means reducing travel constraints may create a chance for casino employees to travel so that they could reduce job stress, therefore raise their job satisfaction. Thirdly, this research divided the research samples into three groups by the degree of job stress. The three groups are low satisfaction group, medium satisfaction group, and high satisfaction group. The means values of these groups were compared by t-test. Results showed that there are significant differences of the means values of interpersonal constraints between low satisfaction group and high satisfaction group. This suggests positive interpersonal relationship especially good family member relationship reduce not only job stress but also travel constraints of casino employees. Interestingly, results of t-test showed there is not a significant difference of the means values of structural constraints between low satisfaction group and high satisfaction group. This suggests structural constraints are outside variables which may be related with tourism destination marketing. Destination marketing organizations (DMO) need use all kinds of tools and techniques to promote their tourism destinations so as to reduce structural constraints of casino employees. This research is significant for both theoretical and practical fields. From the theoretical perspective, the study found the internal relationship between travel constraints, job stress, and job satisfaction and the different roles of three dimensions of travel constraints. From the practical perspective, the study provides useful methods to reduce travel constraints and job stress, therefore, raise job satisfaction of casino employees.

Keywords: hotel, job satisfaction, job stress, travel constraints

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: client classification, loan suitability, risk rating, CART analysis

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Authors: Semaw A., Awet H., Yohannes M.

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Background: Hepatitis B Virus (HBV) is a major global public health problem. Individuals living in Sub-Sahara Africa have 60% lifetime risk of acquiring HBV infection. Evidences showed that 80-90% of those born from infected mothers developed chronic HBV. Perinatal HBV transmission is a major determinant of HBV carrier status, its chronic squeal and maintains HBV transmission across generations. Method: Institution based cross-sectional study was conducted among 406 pregnant mothers attending Antenatal clinics at Mekelle and Ayder referral hospital from January 30 to April 1/2014. Epidata version 3.1 was used for data entry and SPSS version 21 statistical software was used for data cleaning, management and finally determine associated factors of hepatitis B surface antigen adjusting important confounders using multivariable logistic regression analysis at 5% level of significance. Result: The overall prevalence of hepatitis B surface antigen among pregnant women was 33 (8.1%). The socio-demographic characteristic of the study population showed that there is high positivity among secondary school 189 (46.6%). In the multivariable logistic regression analysis, history of a contact with individuals who had history of hepatitis B infection or jaundice and lifetime number of multiple sexual partners were found to be significantly associated with HBsAg positivity at AOR = 3.73 95%C.I (1.373-10.182) and AOR = 2.57 95%C.I (1.173-5.654), respectively. Moreover, Human Immunodeficiency Virus (HIV) and HBV confection rate was found 3.6%. Conclusion: This study has shown that HBV prevalence in pregnant women is highly prevalent (8.1%) in the study area. Contact with individuals who had a history of hepatitis or have jaundice and report of multiple lifetime sexual partnership were associated with hepatitis B infection. Education about HBV transmission and prevention as well as screening all pregnant mothers shall be sought to reduce the serious public health crisis of HBV.

Keywords: HBsAg, hepatitis B, pregnant women, prevalence

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Authors: Ajay Kumar

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Using data from census of India, sample registration system and national family health survey (NFHS-3), this paper traces spatial pattern, trends and the factors which have played their role differently in fertility transition in Uttar Pradesh and Tamil Nadu. For the purpose spatial variation analysis, trend line and binary logistic regression analysis has been carried out. There exist considerable regional disparities in terms of fertility decline in northern and southern states. The pace of fertility decline has been faster in southern and coastal regions, and at a slow pace in backward northern state. In Tamil Nadu fertility declined substantially among the women of lower and higher age groups in comparison to Uttar Pradesh characterized by low literacy, low female age at marriage, poor health infrastructure and low status of women. The Study shows that Fertility rates have been higher among the most vulnerable and deprived sections of the society like Illiterate women, women belong to scheduled caste, scheduled tribe and women residing in rural areas.

Keywords: age specific fertility rate, fertility transition, replacement level, total fertility rate

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Authors: Kawthar F. Albasri, Abdullah M. AlHudaithi, Dana B. AlTurairi, Abdullaziz S. AlQuraini, Adoub Y. AlDerazi, Reem A. Hubail, Haitham A. Jahrami

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Several studies have found that medical students have a significant prevalence of anxiety. The purpose of this review paper is to carefully evaluate the current research on anxiety among medical students in the MENA region and, as a result, estimate the prevalence of these disturbances. Multiple databases, including the CINAHL (Cumulative Index to Nursing and Allied Health Literature), Cochrane Library, Embase, MEDLINE (Medical Literature Analysis and Retrieval System Online), PubMed, PsycINFO (Psychological Information Database), Scopus, Web of Science, UpToDate, ClinicalTrials.gov, WHO Global Health Library, EbscoHost, ProQuest, JAMA Network, and ScienceDirect, were searched. The retrieved article reference lists were rigorously searched and rated for quality. A random effects meta-analysis was performed to compute estimates. The current meta-analysis revealed an alarming estimated pooled prevalence of anxiety (K = 46, N = 27023) of 52.5% [95%CI: 43.3%–61.6%]. A total of 62.0% [95% CI 42.9%; 78.0%] of the students (K = 18, N = 16466) suffered from anxiety during the COVID-19 pandemic, while 52.5% [95% CI 43.3%; 61.6%] had anxiety before COVID-19. Based on the GAD-7 measure, a total of 55.7% [95%CI 30.5%; 78.3%] of the students (K = 10, N = 5830) had anxiety, and a total of 54.7% of the students (K = 18, N = 12154) [95%CI 42.8%; 66.0%] had anxiety using the DASS-21 or 42 measure. Anxiety is a common issue among medical students, making it a genuine problem. Further research should be conducted post-COVD 19, with a focus on anxiety prevention and intervention initiatives for medical students.

Keywords: anxiety, medical students, MENA, meta-analysis, prevalence

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Authors: Ghazal Doostmohammadi, Susan Rahimzadeh

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Aim: This research aimed to investigate the relationship between empathy and interpersonal trust and recognize the mediating role of loneliness between them in both genders. Methods: With a correlational descriptive design, 192 university students (130 female and 62 male) responded to the questionnaires on “empathy quotient,” “loneliness,” and “interpersonal trust” tests. These tests were designed and validated by experts in the field. Data were analysed using Pearson correlation and path analysis, which is a statistical technique that uses standard linear regression equations to determine the degree of conformity of a theoretical causal model with reality. Results: The data analysis showed that there was no significant correlation between interpersonal trust, both with loneliness (t=0.169) and empathy (t=0.186), while there was a significant negative correlation (t=0.359) between empathy and loneliness. This means that there is an inverse correlation between empathy and loneliness. The path analysis confirmed the hypothesis of the research about the mediating role of loneliness between empathy and interpersonal trust. But gender did not play a role in this relationship. Conclusion: As an outcome, clinical professionals and education trainers should pay more attention to interpersonal trust as a basic need and try to recreate and shape it to prevent people's social breakdown, and on the other hand, self-disclosure training (especially in Men), expression of feelings and courage should be given double importance to prevent the consequences of loneliness.

Keywords: empathy, loneliness, interpersonal trust, gender

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Authors: Anusha P. Wijesundara, Dulap I. Rathnayake, Nihal D. Perera

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Bathymetric information is fundamental importance to coastal and marine planning and management, nautical navigation, and scientific studies of marine environments. Satellite-derived bathymetry data provide detailed information in areas where conventional sounding data is lacking and conventional surveys are inaccessible. The two empirical approaches of log-linear bathymetric inversion model and non-linear bathymetric inversion model are applied for deriving bathymetry from high-resolution multispectral satellite imagery. This study compares these two approaches by means of geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. Based on the Levenberg-Marquardt method calibrated the parameters of non-linear inversion model and the multiple-linear regression model was applied to calibrate the log-linear inversion model. In order to calibrate both models, Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Residuals were calculated as the difference between the derived depth values and the validation echo sounder bathymetry data and the geographical distribution of model residuals was mapped. The spatial autocorrelation was calculated by comparing the performance of the bathymetric models and the results showing the geographic errors for both models. A spatial error model was constructed from the initial bathymetry estimates and the estimates of autocorrelation. This spatial error model is used to generate more reliable estimates of bathymetry by quantifying autocorrelation of model error and incorporating this into an improved regression model. Log-linear model (R²=0.846) performs better than the non- linear model (R²=0.692). Finally, the spatial error models improved bathymetric estimates derived from linear and non-linear models up to R²=0.854 and R²=0.704 respectively. The Root Mean Square Error (RMSE) was calculated for all reference points in various depth ranges. The magnitude of the prediction error increases with depth for both the log-linear and the non-linear inversion models. Overall RMSE for log-linear and the non-linear inversion models were ±1.532 m and ±2.089 m, respectively.

Keywords: log-linear model, multi spectral, residuals, spatial error model

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Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

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Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

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Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

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The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: prediction model, sensitivity analysis, simulation method, USMLE

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Authors: Serena Salvi

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Ageistic stereotypes and practices have become a normal and therefore pervasive phenomenon in various aspects of everyday life. Over the past years, renewed awareness towards self-directed age stereotyping in older adults has given rise to a line of research focused on the potential role of attitudes towards ageing on seniors’ health and functioning. This set of studies has showed how a negative internalisation of ageistic stereotypes would discourage older adults in seeking medical advice, in addition to be associated to negative subjective health evaluation. An important dimension of mental health that is often affected in older adults is represented by sleep quality. Self-reported sleep quality among older adults has shown to be often unreliable when compared to their objective sleep measures. Investigations focused on self-reported sleep quality among older adults have suggested how this portion of the population would tend to accept disrupted sleep if believed to be up to standard for their age. On the other hand, unrealistic expectations, and dysfunctional beliefs towards sleep in ageing, might prompt older adults to report sleep disruption even in the absence of objective disrupted sleep. Objective of this study is to examine an association between personal attitudes towards ageing in adults aged 60+ and dysfunctional sleep related cognition. More in detail, this study aims to investigate a potential association between personal attitudes towards ageing, sleep locus of control and dysfunctional beliefs towards sleep among this portion of the population. Data in this study were statistically analysed in SPSS software. Participants were recruited through the online participants recruitment system Prolific. Inclusion of attention check questions throughout the questionnaire and consistency of responses were looked at. Prior to the commencement of this study, Ethical Approval was granted (ref. 39396). Descriptive statistics were used to determine the frequency, mean, and SDs of the variables. Pearson coefficient was used for interval variables, independent T-test for comparing means between two independent groups, analysis of variance (ANOVA) test for comparing the means in several independent groups, and hierarchical linear regression models for predicting criterion variables based on predictor variables. In this study self-perceptions of ageing were assessed using APQ-B’s subscales, while dysfunctional sleep related cognition was operationalised using the SLOC and the DBAS16 scales. Of the final subscales taken in consideration in the brief version of the APQ questionnaire, Emotional Representations (ER), Control Positive (PC) and Control and Consequences Negative (NC) have shown to be of particularly relevance for the remits of this study. Regression analysis show how an increase in the APQ-B subscale Emotional Representations (ER) predicts an increase in dysfunctional beliefs and attitudes towards sleep in this sample, after controlling for subjective sleep quality, level of depression and chronological age. A second regression analysis showed that APQ-B subscales Control Positive (PC) and Control and Consequences Negative (NC) were significant predictors in the change of variance of SLOC, after controlling for subjective sleep quality, level of depression and dysfunctional beliefs about sleep.

Keywords: sleep-related cognition, perceptions of aging, older adults, sleep quality

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Authors: Michiko Iwasaki, Jane M. Endres, Julia Y. Richards, Andrew Futterman

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The present study aimed to examine college students’ happiness during COVID19-pandemic. Using the online survey data from 96 college students in the U.S., a regression analysis was conducted to predict college students’ happiness. The results indicated that a four-predictor model (optimism, college students’ subjective wellbeing, coronavirus stress, and spirituality) explained 57.9% of the variance in student’s subjective happiness, F(4,77)=26.428, p<.001, R2=.579, 95% CI [.41,.66]. The study suggests the importance of learned optimism among college students.

Keywords: COVID-19, optimism, spirituality, well-being

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Authors: Amirmohsen Behjat, Aleck Ostry, Christina Miewald, Bernie Pauly

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Farmers’ Markets have become one of the important healthy food suppliers in both rural communities and urban settings. Farmers’ markets are evolving and their number has rapidly increased in the past decade. Despite this drastic increase, the distribution of the farmers’ markets is not even across different areas. The main goal of this study is to explore the socioeconomic, geographic, and demographic variables which affect the establishment of farmers’ market in rural communities in British Columbia (BC). Thus, the data on available farmers’ markets in rural areas were collected from BC Association of Farmers’ Markets and spatially joined to BC map at Dissemination Area (DA) level using ArcGIS software to link the farmers’ market to the respective communities that they serve. Then, in order to investigate this issue and understand which rural communities farmer’ markets tend to operate, a binary logistic regression analysis was performed with the availability of farmer’ markets at DA-level as dependent variable and Deprivation Index (DI), Metro Influence Zone (MIZ) and population as independent variables. The results indicated that DI and MIZ variables are not statistically significant whereas the population is the only which had a significant contribution in predicting the availability of farmers’ markets in rural BC. Moreover, this study found that farmers’ markets usually do not operate in rural food deserts where other healthy food providers such as supermarkets and grocery stores are non-existent. In conclusion, the presence of farmers markets is not associated with socioeconomic and geographic characteristics of rural communities in BC, but farmers’ markets tend to operate in more populated rural communities in BC.

Keywords: farmers’ markets, socioeconomic and demographic variables, metro influence zone, logistic regression, ArcGIS

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Authors: Corina E. Niculaescu, Ivan Sangiorgi, Adrian R. Bell

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The COVID-19 pandemic has been life-changing in many aspects of people’s daily and social lives, but has it also changed attitudes towards investments? This paper explores the effect of the COVID-19 pandemic on retail investors’ levels of investments in the U.S. during the first COVID-19 wave in summer 2020. This is an unprecedented health crisis, which could lead to changes in investment behavior, including irrational behavior in retail investors. As such, this study aims to inform policymakers of what happened to investment decisions during the COVID-19 pandemic so that they can protect retail investors during extreme events like a global health crisis. The study aims to answer two research questions. First, was the level of investments affected by the COVID-19 pandemic, and if so, why? Second, how were investments affected by retail investors’ personal experience with COVID-19? The research analysis is based on primary survey data collected on the Amazon Mechanical Turk platform from a representative sample of U.S. respondents. Responses were collected between the 15th of July and 28th of August 2020 from 1,148 U.S. retail investors who hold mutual fund investments and a savings account. The research explores whether being affected by COVID-19, change in the level of savings, and risk capacity can explain the change in the level of investments by using regression analysis. The dependent variable is changed in investments measured as decrease, no change, and increase. For this reason, the methodology used is ordered probit regression models. The results show that retail investors in the U.S. increased their investments during the first wave of COVID-19, which is unexpected as investors are usually more cautious in crisis times. Moreover, the study finds that those who were affected personally by COVID-19 (e.g., tested positive) were more likely to increase their investments, which is irrational behavior and contradicts expectations. An increase in the level of savings and risk capacity was also associated with increased investments. Overall, the findings show that having personal experience with a health crisis can have an impact on one’s investment decisions as well. Those findings are important for both retail investors and policymakers, especially now that online trading platforms have made trading easily accessible to everyone. There are risks and potential irrational behaviors associated with investment decisions during times of crisis, and it is important that retail investors are aware of them before making financial decisions.

Keywords: COVID-19, financial decision-making, health crisis retail investors, survey

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Authors: Ndidi F. Amulu, Patrick E. Amulu, Gordian O. Mbah, Callistus N. Ude

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Comparative analysis of the properties of melon seed, coconut fruit and their oil yield were evaluated in this work using standard analytical technique AOAC. The results of the analysis carried out revealed that the moisture contents of the samples studied are 11.15% (melon) and 7.59% (coconut). The crude lipid content are 46.10% (melon) and 55.15% (coconut).The treatment combinations used (leaching time, leaching temperature and solute: solvent ratio) showed significant difference (p < 0.05) in yield between the samples, with melon oil seed flour having a higher percentage range of oil yield (41.30 – 52.90%) and coconut (36.25 – 49.83%). The physical characterization of the extracted oil was also carried out. The values gotten for refractive index are 1.487 (melon seed oil) and 1.361 (coconut oil) and viscosities are 0.008 (melon seed oil) and 0.002 (coconut oil). The chemical analysis of the extracted oils shows acid value of 1.00mg NaOH/g oil (melon oil), 10.050mg NaOH/g oil (coconut oil) and saponification value of 187.00mg/KOH (melon oil) and 183.26mg/KOH (coconut oil). The iodine value of the melon oil gave 75.00mg I2/g and 81.00mg I2/g for coconut oil. A standard statistical package Minitab version 16.0 was used in the regression analysis and analysis of variance (ANOVA). The statistical software mentioned above was also used to optimize the leaching process. Both samples gave high oil yield at the same optimal conditions. The optimal conditions to obtain highest oil yield ≥ 52% (melon seed) and ≥ 48% (coconut seed) are solute - solvent ratio of 40g/ml, leaching time of 2hours and leaching temperature of 50oC. The two samples studied have potential of yielding oil with melon seed giving the higher yield.

Keywords: Coconut, Melon, Optimization, Processing

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Authors: Mary Okonkwo Ekwy

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The problem of the growing disillusionment of College of Education teachers with academic life vis-à-vis their job satisfaction and commitment was investigated in this study with a view to finding out if both their job satisfaction and commitment have suffered, and to find out if there was a relationship between job satisfaction and commitment among these College of Education teachers. Due consideration was also given in the study to the possible effects of demographic variables on attitudes to their job. To carry out a study of job satisfaction and commitment among the College of Education teachers and to explore the relationship between them, research instruments were used for measuring the levels of job satisfaction and commitment among them. A sample of 200 Colleges of Education teachers, comprising 15 Professors, 9 Principal Lecturers, 70 Senior Lecturer and 106 Lecturers was used for the study. Five major hypothesis were tested with regard to the relationship between job satisfaction and commitment among the teachers. The Pearson correlation, the F-ratio, and regression analysis were used for data analysis and hypothesis testing. The result of this investigation suggests that, perhaps the best way to secure the commitment of teachers is to ensure their job satisfaction. Future investigations will further enrich our knowledge about these very important themes.

Keywords: job satisfaction, commitment, academic staff, college of education

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Authors: H. Nouri, I. E. Achouri, A. Grimes, H. Ait Said, M. Aissou, Y. Zebboudj

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This paper aims to analysis the behaviour of DC corona discharge in wire-to-plate electrostatic precipitators (ESP). Current-voltage curves are particularly analysed. Experimental results show that discharge current is strongly affected by the applied voltage. The proposed method of current identification is to use the method of least squares. Least squares problems that of into two categories: linear or ordinary least squares and non-linear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. A closed-form solution (or closed form expression) is any formula that can be evaluated in a finite number of standard operations. The non-linear problem has no closed-form solution and is usually solved by iterative.

Keywords: electrostatic precipitator, current-voltage characteristics, least squares method, electric field, magnetic field

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Authors: Teerada Apibunyopas, Nithinant Thammakoranonta

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Many organizations bring e-Learning to use as a tool in their training and human development department. It is getting more popular because it is easy to access to get knowledge all the time and also it provides a rich content, which can develop the employees skill efficiently. This study focused on the factors that affect using e-Learning efficiently, so it will make job satisfaction increased. The questionnaires were sent to employees in large commercial banks, which use e-Learning located in Bangkok, the results from multiple linear regression analysis showed that employee’s characteristics, characteristics of e-Learning, learning and growth have influence on job satisfaction.

Keywords: e-Learning, job satisfaction, learning and growth, Bangkok

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Authors: Chander Shekhar, Jeetendra Yadav, Shaziya Allarakha

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Hypertension has been described as an 'iceberg disease' as those who suffered are ignored and hence usually seek healthcare services at a very late stage. It is estimated that more than 2 million Indians are suffering from hypertensive heart disease that contributed to above 0.13 million deaths in 2016. The paper study aims to know the prevalence of Hypertension in India and its variation by socioeconomic backgrounds and to find out risk factors discriminating hypertension with special emphasis on consumption of tobacco and alcohol among men aged 15-54 years in India. The paper uses NFHS (2015-16) data. The paper used binary logistic regression and discriminant analysis to find significant predictors and discriminants of interest. The prevalence of hypertension was 16.5% in the study population. The results suggest that consumption of alcohol and tobacco are significant discriminant characteristics in carrying hypertension irrespective of what socioeconomic background characteristic he possesses.

Keywords: hypertention, alcohol, tobacco, discriminant

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Authors: Dongjoo Chin

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The purpose of this study was to investigate the effectiveness of an Early Intensive Behavioral Intervention (EIBI) program on infants with autism spectrum disorder (ASD) and to explore the factors predicting the effectiveness of the program, focusing on the infant's age, language ability, problem behaviors, and parental stress. 19 pairs of infants aged between 2 and 5 years who have had been diagnosed with ASD, and their parents participated in an EIBI program at a clinic providing evidence-based treatment based on applied behavior analysis. The measurement tools which were administered before and after the EIBI program and compared, included PEP-R, a curriculum evaluation, K-SIB-R, K-Vineland-II, K-CBCL, and PedsQL for the infants, and included PSI-SF and BDI-II for the parents. Statistical analysis was performed using a sample t-test and multiple regression analysis and the results were as follows. The EIBI program showed significant improvements in overall developmental age, curriculum assessment, and quality of life for infants. There was no difference in parenting stress or depression. Furthermore, measures for both children and parents at the start of the program predicted neither PEP-R nor the degree of improvement in curriculum evaluation measured six months later at the end of the program. Based on these results, the authors suggest future directions for developing an effective intensive early intervention (EIBI) program for infants with ASD in Korea, and discuss the implications and limitations of this study.

Keywords: applied behavior analysis, autism spectrum disorder, early intensive behavioral intervention, parental stress

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Authors: Muhammad Tariq, Hammad Tahir, Fawwad Mahmood Butt

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Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model.

Keywords: forecasting, time series, auto regression, ARCH, ARMA

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Authors: K. Assis, B. Bonaventure, A. Abdul Rahim, H. Affendy, A. Mohammad Amizi

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Oil palm smallholders are considered as a very important producer of oil palm in Malaysia. They are categorized into two, which are organized smallholder and independent smallholder. In this study, there were 1000 oil palms smallholders have been interviewed by using a structured questionnaire. The main objective of the survey is to identify the relationship between socioeconomic characteristics of smallholders with their knowledge, attitude, and practices toward Ganoderma disease. The locations of study include Peninsular Malaysia and Sabah. There were three important aspects studied, namely knowledge of Ganoderma disease, attitude towards the disease as well as the practices in managing the disease. Cluster analysis, factor analysis, and binary logistic regression were used to analyze the data collected. The findings of the study should provide a baseline data which can be used by the relevant agencies to conduct programs or to formulate a suitable development plan to improve the knowledge, attitude and practices of oil palm smallholders in managing Ganoderma disease.

Keywords: attitude, Ganoderma, knowledge, oil palm, practices, smallholders

Procedia PDF Downloads 378

Authors: Neil Jordan M. Uy, Fe Pelilia V. Hernandez

Abstract:

A counselor is always regarded as a professional who embodies the willingness to help others through the process of counseling. He is knowledgeable and skillful of the different theories, tools, and techniques that are useful in aiding the client to cope with their dilemmas. The negative experiences of the clients that are shared during the counseling session can affect the professional counselor. Compassion fatigue, a professional impairment, is characterized by the decline of one’s productivity and the feeling of anxiety and stress brought about as the counselor empathizes, listens, and cares for others. This descriptive type of research aimed to explore variables that are predictors of compassion fatigue utilizing three research instruments; Demographic Profile Sheet, Professional Quality of Life Scale, and Neo-Pi-R. The 52 respondents of this study were counselors from the different Dominican schools in the Philippines. Generally, the counselors have low level of compassion fatigue across personal characteristics (age, gender, years of service, highest educational attainment, and professional status) and personality traits (extraversion, agreeableness, conscientiousness, openness, and neuroticism). ANOVA validated the findings of this that among the personal characteristics and personality traits, extraversion with f-value of 3.944 and p-value of 0.026, and conscientiousness, with f-value of 4.125 and p-value of 0.022 were found to have significant difference in the level of compassion fatigue. A very significant difference was observed with neuroticism with f-value of 6.878 and p-value 0.002. Among the personal characteristics and personal characteristics, only neuroticism was found to predict compassion fatigue. The computed r2 value of 0.204 using multiple regression analysis suggests that 20.4 percent of compassion fatigue can be predicted by neuroticism. The predicting power of neuroticism can be computed from the regression model Y=0.156x+26.464; where x is the number of neuroticism.

Keywords: big five personality traits, compassion fatigue, counselors, professional quality of life scale

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Authors: C. C. Chima, C. J. M. Anumaka

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This study evaluates environmental disclosures on the financial performance of quoted industrial goods manufacturing sectors in Nigeria. The study employed a quasi-experimental research design to establish the relationship that exists between the environmental disclosure index and financial performance indices (return on assets - ROA, return on equity - ROE, and earnings per share - EPS). A purposeful sampling technique was employed to select five (5) industrial goods manufacturing sectors quoted on the Nigerian Stock Exchange. Secondary data covering 2011 to 2020 financial years were extracted from annual reports of the study sectors using a content analysis method. The data were analyzed using SPSS, Version 23. Panel Ordinary Least Squares (OLS) regression method was employed in estimating the unknown parameters in the study’s regression model after conducting diagnostic and preliminary tests to ascertain that the data set are reliable and not misleading. Empirical results show that there is an insignificant negative relationship between the environmental disclosure index (EDI) and the performance indices (ROA, ROE, and EPS) of the industrial goods manufacturing sectors in Nigeria. The study recommends that: only relevant information which increases the performance indices should appear on the disclosure checklist; environmental disclosure practices should be country-specific; and company executives in Nigeria should increase and monitor the level of investment (resources, time, and energy) in order to ensure that environmental disclosure has a significant impact on financial performance.

Keywords: earnings per share, environmental disclosures, return on assets, return on equity

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Authors: Slim Chokri

Abstract:

Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

Procedia PDF Downloads 418

Authors: Sait Ozsoy, Hacer Yasar Teke, Mustafa Dalgic, Cetin Ketenci, Ertugrul Gok, Kenan Karbeyaz, Azem Irez, Mesut Akyol

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Self-mutilating behavior or self-injury behavior (SIB) is defined as: intentional harm to one’s body without intends to commit suicide”. SIB cases are commonly seen in psychiatry and forensic medicine practices. Despite variety of SIB methods, cuts in the skin is the most common (70-97%) injury in this group of patients. Subjects with SIB have one or more other comorbidities which include depression, anxiety, depersonalization, and feeling of worthlessness, borderline personality disorder, antisocial behaviors, and histrionic personality. These individuals feel a high level of hostility towards themselves and their surroundings. Researches have also revealed a strong relationship between antisocial personality disorder, criminal behavior, and SIB. This study has retrospectively evaluated 6,599 autopsy cases performed at forensic medicine institutes of six major cities (Ankara, Izmir, Diyarbakir, Erzurum, Trabzon, Eskisehir) of Turkey in 2013. The study group consisted of all cases with SIB findings (psychopathic cuts, cigarette burns, scars, and etc.). The relationship between causes of death in the study group (SIB subjects) and the control group was investigated. The control group was created from subjects without signs of SIB. Mann-Whitney U test was used for age variables and Chi-square test for categorical variables. Multinomial logistic regression analysis was used in order to analyze group differences in respect to manner of death (natural, accident, homicide, suicide) and analysis of risk factors associated with each group was determined by the Binomial logistic regression analysis. This study used SPSS statistics 15.0 for all its statistical and calculation needs. The statistical significance was p <0.05. There was no significant difference between accidental and natural death among the groups (p=0.737). Also there was a unit increase in number of cuts in psychopathic group while number of accidental death decreased (95% CI: 0.941-0.993) by 0.967 times (p=0.015). In contrast, there was a significant difference between suicidal and natural death (p<0.001), and also between homicidal and natural death (p=0.025). SIB is often seen with borderline and antisocial personality disorder but may be associated with many psychiatric illnesses. Studies have shown a relationship between antisocial personality disorders with criminal behavior and SIB with suicidal behavior. In our study, rate of suicide, murder and intoxication was higher compared to the control group. It could be concluded that SIB can be used as a predictor of possibility of one’s harm to him/herself and other people.

Keywords: autopsy, cause of death, forensic science, self-injury behaviour

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Authors: Firouz Amani, Adalat Hoseinian, Sajjad Hakimian

Abstract:

Background: The mortality due to Myocardial Infarction (MI) is often occur during the first hours after onset of symptom. So, for taking the necessary treatment and decreasing the mortality rate, timely visited of the hospital could be effective in this regard. The aim of this study was to investigate the impact of effective factors in mortality of MI patients by using Linear Logistic Regression. Materials and Methods: In this case-control study, all patients with Acute MI who referred to the Ardabil city hospital were studied. All of died patients were considered as the case group (n=27) and we select 27 matched patients without Acute MI as a control group. Data collected for all patients in two groups by a same checklist and then analyzed by SPSS version 24 software using statistical methods. We used the linear logistic regression model to determine the effective factors on mortality of MI patients. Results: The mean age of patients in case group was significantly higher than control group (75.1±11.7 vs. 63.1±11.6, p=0.001).The history of non-cardinal diseases in case group with 44.4% significantly higher than control group with 7.4% (p=0.002).The number of performed PCIs in case group with 40.7% significantly lower than control group with 74.1% (P=0.013). The time distance between hospital admission and performed PCI in case group with 110.9 min was significantly upper than control group with 56 min (P=0.001). The mean of delay time from Onset of symptom to hospital admission (patient delay) and the mean of delay time from hospital admissions to receive treatment (system delay) was similar between two groups. By using logistic regression model we revealed that history of non-cardinal diseases (OR=283) and the number of performed PCIs (OR=24.5) had significant impact on mortality of MI patients in compare to other factors. Conclusion: Results of this study showed that of all studied factors, the number of performed PCIs, history of non-cardinal illness and the interval between onset of symptoms and performed PCI have significant relation with morality of MI patients and other factors were not meaningful. So, doing more studies with a large sample and investigated other involved factors such as smoking, weather and etc. is recommended in future.

Keywords: acute MI, mortality, heart failure, arrhythmia

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Authors: Zakaria S. G. Hegazy, Walid M. A. Ahmed

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This paper examines the dependence structure of Islamic stock markets on Bitcoin’s realized volatility components in bear, normal, and bull market periods. A quantile regression approach is employed, after adjusting raw returns with respect to a broad set of relevant global factors and accounting for structural breaks in the data. The results reveal that upside volatility tends to exert negative influences on Islamic developed-market returns more in bear than in bull market conditions, while downside volatility positively affects returns during bear and bull conditions. For emerging markets, we find that the upside (downside) component exerts lagged negative (positive) effects on returns in bear (all) market regimes. By and large, the dependence structures turn out to be asymmetric. Our evidence provides essential implications for investors.

Keywords: cryptocurrency markets, bitcoin, realized volatility measures, asymmetry, quantile regression

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Authors: Kitjapat Phuvoravan, Phattaraphong Ponsorn

Abstract:

In the design of Castellated beams (CB), the web post buckling acted by horizontal shear force is one of the important failure modes that have to be considered. It is also a dominant governing mode when design following the AISC 31 design guideline which is just published. However, the equation of the web post buckling given by the guideline is still questionable for most of the engineers. So the purpose of this paper is to study and provide a proposed equation for design the web post buckling with more simplified and convenient to use. The study is also including the improper of the safety factor given by the guideline. The proposed design equation is acquired by regression method based on the results of finite element analysis. An amount of Cellular beam simulated to study is modelled by using shell element, analysis with both geometric and material nonlinearity. The results of the study show that the use of the proposed equation to design the web post buckling in Castellated beams is more simple and precise for computation than the equations provided from the guideline.

Keywords: castellated beam, web opening, web post buckling, design equation

Procedia PDF Downloads 280